



'''simple docstring'''



import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Union[str, Any]						  =					(IPNDMScheduler,)
						_lowerCAmelCase  : Dict						  =					(('num_inference_steps', 5_0),)







						def        __lowercase     (	self :   List[str]					,     **lowerCAmelCase__ :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      {'''num_train_timesteps''': 10_00}
													config.update(**lowerCAmelCase__					)
													return config







						def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   Union[str, Any]=0					,     **lowerCAmelCase__ :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      dict(self.forward_default_kwargs					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      kwargs.pop('''num_inference_steps'''					,     lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      self.dummy_sample
													SCREAMING_SNAKE_CASE       :   List[str]         =      0.1 * sample
													SCREAMING_SNAKE_CASE       :   Dict         =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

													for scheduler_class in self.scheduler_classes:
																				SCREAMING_SNAKE_CASE       :   str         =      self.get_scheduler_config(**lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      scheduler_class(**lowerCAmelCase__					)
																				scheduler.set_timesteps(lowerCAmelCase__					)
																				# copy over dummy past residuals
																				SCREAMING_SNAKE_CASE       :   int         =      dummy_past_residuals[:]

																				if time_step is None:
																											SCREAMING_SNAKE_CASE       :   Dict         =      scheduler.timesteps[len(scheduler.timesteps					) // 2]

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Optional[Any]         =      scheduler_class.from_pretrained(lowerCAmelCase__					)
																											new_scheduler.set_timesteps(lowerCAmelCase__					)
																											# copy over dummy past residuals
																											SCREAMING_SNAKE_CASE       :   Any         =      dummy_past_residuals[:]

																				SCREAMING_SNAKE_CASE       :   Dict         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																				SCREAMING_SNAKE_CASE       :   int         =      new_scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample

																				assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      new_scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample

																				assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"







						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													pass







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   Any=0					,     **lowerCAmelCase__ :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      dict(self.forward_default_kwargs					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      kwargs.pop('''num_inference_steps'''					,     lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.dummy_sample
													SCREAMING_SNAKE_CASE       :   List[str]         =      0.1 * sample
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

													for scheduler_class in self.scheduler_classes:
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.get_scheduler_config()
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      scheduler_class(**lowerCAmelCase__					)
																				scheduler.set_timesteps(lowerCAmelCase__					)

																				# copy over dummy past residuals (must be after setting timesteps)
																				SCREAMING_SNAKE_CASE       :   int         =      dummy_past_residuals[:]

																				if time_step is None:
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      scheduler.timesteps[len(scheduler.timesteps					) // 2]

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Tuple         =      scheduler_class.from_pretrained(lowerCAmelCase__					)
																											# copy over dummy past residuals
																											new_scheduler.set_timesteps(lowerCAmelCase__					)

																											# copy over dummy past residual (must be after setting timesteps)
																											SCREAMING_SNAKE_CASE       :   Any         =      dummy_past_residuals[:]

																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																				SCREAMING_SNAKE_CASE       :   List[str]         =      new_scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample

																				assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

																				SCREAMING_SNAKE_CASE       :   Tuple         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																				SCREAMING_SNAKE_CASE       :   Tuple         =      new_scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample

																				assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"







						def        __lowercase     (	self :   Optional[int]					,     **lowerCAmelCase__ :   Any					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[str]         =      self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE       :   int         =      self.get_scheduler_config(**lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      scheduler_class(**lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      10
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.dummy_model()
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.dummy_sample_deter
													scheduler.set_timesteps(lowerCAmelCase__					)

													for i, t in enumerate(scheduler.timesteps					):
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model(lowerCAmelCase__					,     lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Dict         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					).prev_sample

													for i, t in enumerate(scheduler.timesteps					):
																				SCREAMING_SNAKE_CASE       :   Any         =      model(lowerCAmelCase__					,     lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					).prev_sample

													return sample







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      dict(self.forward_default_kwargs					)

													SCREAMING_SNAKE_CASE       :   int         =      kwargs.pop('''num_inference_steps'''					,     lowerCAmelCase__					)

													for scheduler_class in self.scheduler_classes:
																				SCREAMING_SNAKE_CASE       :   int         =      self.get_scheduler_config()
																				SCREAMING_SNAKE_CASE       :   Tuple         =      scheduler_class(**lowerCAmelCase__					)

																				SCREAMING_SNAKE_CASE       :   str         =      self.dummy_sample
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      0.1 * sample

																				if num_inference_steps is not None and hasattr(lowerCAmelCase__					,     '''set_timesteps'''					):
																											scheduler.set_timesteps(lowerCAmelCase__					)
																				elif num_inference_steps is not None and not hasattr(lowerCAmelCase__					,     '''set_timesteps'''					):
																											SCREAMING_SNAKE_CASE       :   int         =      num_inference_steps

																				# copy over dummy past residuals (must be done after set_timesteps)
																				SCREAMING_SNAKE_CASE       :   Any         =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
																				SCREAMING_SNAKE_CASE       :   List[str]         =      dummy_past_residuals[:]

																				SCREAMING_SNAKE_CASE       :   int         =      scheduler.timesteps[5]
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      scheduler.timesteps[6]

																				SCREAMING_SNAKE_CASE       :   str         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																				SCREAMING_SNAKE_CASE       :   str         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample

																				self.assertEqual(output_a.shape					,     sample.shape					)
																				self.assertEqual(output_a.shape					,     output_a.shape					)

																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     **lowerCAmelCase__					).prev_sample

																				self.assertEqual(output_a.shape					,     sample.shape					)
																				self.assertEqual(output_a.shape					,     output_a.shape					)







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													for timesteps in [1_00, 10_00]:
																				self.check_over_configs(num_train_timesteps=lowerCAmelCase__					,     time_step=lowerCAmelCase__					)







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													for t, num_inference_steps in zip([1, 5, 10]					,     [10, 50, 1_00]					):
																				self.check_over_forward(num_inference_steps=lowerCAmelCase__					,     time_step=lowerCAmelCase__					)







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.full_loop()
													SCREAMING_SNAKE_CASE       :   List[str]         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

													assert abs(result_mean.item() - 2_54_05_29					) < 10









'''simple docstring'''



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_tensorflow_text_available():
							from transformers.models.bert import TFBertTokenizer


lowerCAmelCase_  :							List[str]             =    ['bert-base-uncased', 'bert-base-cased']
lowerCAmelCase_  :							int             =    'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():
							class 						lowerCamelCase_				(       tf.keras.Model      ):







													def __init__(	self :   Optional[int]					,     lowerCAmelCase__ :   Optional[Any]					):

																				"""simple docstring"""
																				super().__init__()
																				SCREAMING_SNAKE_CASE       :   List[str]         =      tokenizer
																				SCREAMING_SNAKE_CASE       :   str         =      AutoConfig.from_pretrained(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      TFAutoModel.from_config(lowerCAmelCase__					)







													def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   Tuple					):

																				"""simple docstring"""
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.tokenizer(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   str         =      self.bert(**lowerCAmelCase__					)
																				return out["pooler_output"]
@require_tf
@require_tensorflow_text
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													super().setUp()

													SCREAMING_SNAKE_CASE       :   int         =      [
													    BertTokenizer.from_pretrained(lowerCAmelCase__					) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
													]  # repeat for when fast_bert_tokenizer=false
													SCREAMING_SNAKE_CASE       :   Dict         =      [TFBertTokenizer.from_pretrained(lowerCAmelCase__					) for checkpoint in TOKENIZER_CHECKPOINTS] + [
													    TFBertTokenizer.from_pretrained(lowerCAmelCase__					,     use_fast_bert_tokenizer=lowerCAmelCase__					)
													    for checkpoint in TOKENIZER_CHECKPOINTS
													]
													assert len(self.tokenizers					) == len(self.tf_tokenizers					)

													SCREAMING_SNAKE_CASE       :   Any         =      [
													    '''This is a straightforward English test sentence.''',
													    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
													    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
													    '''And some much more rare Chinese: 齉 堃 齉堃''',
													    '''Je vais aussi écrire en français pour tester les accents''',
													    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
													]
													SCREAMING_SNAKE_CASE       :   int         =      list(zip(self.test_sentences					,     self.test_sentences[::-1]					)					)







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													for tokenizer, tf_tokenizer in zip(self.tokenizers					,     self.tf_tokenizers					):
																				for test_inputs in (self.test_sentences, self.paired_sentences):
																											SCREAMING_SNAKE_CASE       :   Tuple         =      tokenizer(lowerCAmelCase__					,     return_tensors='''tf'''					,     padding='''longest'''					)
																											SCREAMING_SNAKE_CASE       :   List[Any]         =      tf_tokenizer(lowerCAmelCase__					)

																											for key in python_outputs.keys():
																																		self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape					)					)
																																		self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]					,     tf.intaa					) == tf_outputs[key]					)					)







						@slow
						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													for tf_tokenizer in self.tf_tokenizers:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      tf_tokenizer(self.paired_sentences					)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      tf_tokenizer(
																				    text=[sentence[0] for sentence in self.paired_sentences]					,     text_pair=[sentence[1] for sentence in self.paired_sentences]					,     )
																				for key in merged_outputs.keys():
																											self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]					,     tf.intaa					) == separated_outputs[key]					)					)







						@slow
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													for tf_tokenizer in self.tf_tokenizers:
																				SCREAMING_SNAKE_CASE       :   Any         =      tf.function(lowerCAmelCase__					)
																				for test_inputs in (self.test_sentences, self.paired_sentences):
																											SCREAMING_SNAKE_CASE       :   int         =      tf.constant(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Any         =      compiled_tokenizer(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   List[str]         =      tf_tokenizer(lowerCAmelCase__					)

																											for key in eager_outputs.keys():
																																		self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]					)					)







						@slow
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													for tf_tokenizer in self.tf_tokenizers:
																				SCREAMING_SNAKE_CASE       :   int         =      ModelToSave(tokenizer=lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Tuple         =      tf.convert_to_tensor(self.test_sentences					)
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      model(lowerCAmelCase__					)  # Build model with some sample inputs
																				with TemporaryDirectory() as tempdir:
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      Path(lowerCAmelCase__					) / '''saved.model'''
																											model.save(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      tf.keras.models.load_model(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Tuple         =      loaded_model(lowerCAmelCase__					)
																				# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																				self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output					)					)					,     1e-5					)




